Query Interval Research Articles

Efficiently answer top-k queries on typed intervals

Consider a database consisting of a set of tuples, each of which contains an interval, a type and a weight. These tuples are called typed intervals and used to support applications involving diverse intervals. In this paper, we study top-k queries on typed intervals. The query reports k intervals intersecting the query time, containing a particular type and having the largest weight. The query time can be a point or an interval. Further, we define top-k continuous queries that return qualified intervals at each time point during the query interval. To efficiently answer such queries, a key challenge is to build an index structure to manage typed intervals. Employing the standard interval tree, we build the structure in a compact way to reduce the I/O cost, and provide analytically derived partitioning methods to manage the data. Query algorithms are proposed to support point, interval and continuous queries. An auxiliary main-memory structure is developed to report continuous results. Using large real and synthetic datasets, extensive experiments are performed in a prototype database system to demonstrate the effectiveness, efficiency and scalability. The results show that our method significantly outperforms alternative methods in most settings.

XStore: Federated temporal query processing for large scale RDF triples on a cloud environment

Temporal information retrieval tasks have a long history in information retrieval field and also have attracted neuroscientists working on memory system. It becomes more important in Semantic Web where structured data in RDF triples, often with temporal information, are rapidly accumulated over time. Existing triple stores already support loading RDF triples and answering a given SPARQL query with time interval constraints. However, few triple stores has been optimized for processing time interval queries which are important for temporal information retrieval tasks. In this paper, we propose xStore, a federated SPARQL engine running on a cloud environment, which supports a fast processing of temporal queries. xStore is built on top of heterogeneous storages such as key-value stores and conventional triple stores. Experiments over real-world temporal datasets showed that our approach is faster than a conventional SPARQL engine for processing temporal queries.

SeqLib: a C ++ API for rapid BAM manipulation, sequence alignment and sequence assembly

SeqLib is available on Linux and OSX for the C ++98 standard and later at github.com/walaj/SeqLib. SeqLib is released under the Apache2 license. Additional capabilities for BLAT alignment are available under the BLAT license. jwala@broadinstitue.org ; rameen@broadinstitute.org.

Efficient indexing structure to handle durable queries through web crawling

This paper studies efficient processing of durable top-k queries on historical time series databases. Durable top-k queries, obtained as an extension of snapshot top-k queries during a certain time period, play a key role in finding objects with durable quality and predicting the status of these objects for successive time intervals by updating the query interval at all timestamps. Web crawling and indexing are tremendously significant in recent times, especially in terms of achieving efficient durable top-k queries from vast quantum of web documents. Existing algorithms that have been employed throw up results that are less than applicable to analyzers. This paper chiefly focuses on web crawling and indexing query terms under their respective categories and updating rank changes at every time interval. Links are crawled using the modified depth-first search (MDFS) algorithm, accessed, and metadata such as the title, keywords, and descriptions extracted. To handle query indexing, novel indexing techniques are proposed to yield efficient results. This study is invaluable for analysts working on large data obtained as a result of crawling and indexing, effectively decreasing their workload.

Attacking NTP's Authenticated Broadcast Mode

We identify two attacks on the Network Time Protocol (NTP)'s cryptographically-authenticated broadcast mode. First, we present a replay attack that allows an on-path attacker to indefinitely stick a broadcast client to a specific time. Second, we present a denial-of-service (DoS) attack that allows an off-path attacker to prevent a broadcast client from ever updating its system clock; to do this, the attacker sends the client a single malformed broadcast packet per query interval. Our DoS attack also applies to all other NTP modes that are `ephemeral' or `preemptable' (including manycast, pool, etc). We then use network measurements to give evidence that NTP's broadcast and other ephemeral/preemptable modes are being used in the wild. We conclude by discussing why NTP's current implementation of symmetric-key cryptographic authentication does not provide security in broadcast mode, and make some recommendations to improve the current state of affairs.

A Novel Distributed Index Method for Cloud Computing

For the performance and maintenance cost of the existing index methods, this paper presents a distributed multi access entrance B+ tree index method, which has some features that are important in practice, namely (1) achieve efficient parallel interval queries, and (2) and low maintenance cost for index structure. Our scheme relies on four key techniques to achieve efficient parallel interval queries and low maintenance cost for index structure: (1) It gives a routing table to each leaf node of distributed B+ tree, so as to interval search can be completed from any leaf node of any storage node and break through the Bottleneck caused by the root node in the distributed B+ tree; (2) It construction of balanced binary tree in different levels of a node, and choose select the relevant nodes for its routing table; (3) It uses the layer-by-layer transitivity of B+ tree node splitting information to sense node split position so as to only update routing information within subtree of corresponding particle at the time of node splitting; (4) it uses balanced structure of B+ trees to realize only update the single route information of relevant nodes at the time of node splitting, without adjusting the route tables in a large area. Our scheme has been implemented and evaluated, and the performance results are encouraging.

Efficient Queries of Stand-off Annotations for Natural Language Processing on Electronic Medical Records.

In natural language processing, stand-off annotation uses the starting and ending positions of an annotation to anchor it to the text and stores the annotation content separately from the text. We address the fundamental problem of efficiently storing stand-off annotations when applying natural language processing on narrative clinical notes in electronic medical records (EMRs) and efficiently retrieving such annotations that satisfy position constraints. Efficient storage and retrieval of stand-off annotations can facilitate tasks such as mapping unstructured text to electronic medical record ontologies. We first formulate this problem into the interval query problem, for which optimal query/update time is in general logarithm. We next perform a tight time complexity analysis on the basic interval tree query algorithm and show its nonoptimality when being applied to a collection of 13 query types from Allen’s interval algebra. We then study two closely related state-of-the-art interval query algorithms, proposed query reformulations, and augmentations to the second algorithm. Our proposed algorithm achieves logarithmic time stabbing-max query time complexity and solves the stabbing-interval query tasks on all of Allen’s relations in logarithmic time, attaining the theoretic lower bound. Updating time is kept logarithmic and the space requirement is kept linear at the same time. We also discuss interval management in external memory models and higher dimensions.

The impacts of time constraint on users' search strategy during search process

ABSTRACTThis study examined the effects of time constraints on searchers' information search strategies during search process, particularly at two search stages (first round and end point). A user experiment with forty participants was conducted, and each participant was asked to search with and without time constraint. The results showed that time constraint had significant effect on users' first/mean dwell time on search engine result pages (SERPs) during the first query interval; however, time constraint did not influence their dwell time on SERPs or content pages when the whole session was considered, and it only had significant effect on the number of pages viewed per query. The findings indicated that users did employ different search strategies when searching with and without time constraint, and their search strategies changed over time within the search session. Generally, when there was no time constraint, users tended to employ economic‐style search strategy at the beginning of search; but when given time constraint, they became more selective and cautious in examining the search results. The findings of this study have implications for search system design to assist searchers under time constraint and help them search more effectively and efficiently.

Computational Cost Reduction of Nondominated Sorting Using the M-Front

Many multiobjective evolutionary algorithms rely on the nondominated sorting procedure to determine the relative quality of individuals with respect to the population. In this paper, we propose a new method to decrease the cost of this procedure. Our approach is to determine the nondominated individuals at the start of the evolutionary algorithm run and to update this knowledge as the population changes. In order to do this efficiently, we propose a special data structure called the M-front, to hold the nondominated part of the population. The M-front uses the geometric and algebraic properties of the Pareto dominance relation to convert orthogonal range queries into interval queries using a mechanism based on the nearest neighbor search. These interval queries are answered using dynamically sorted linked lists. Experimental results show that our method can perform significantly faster than the state-of-the-art Jensen–Fortin’s algorithm, especially in many-objective scenarios. A significant advantage of our approach is that, if we change a single individual in the population we still know which individuals are dominated and which are not.

Lower Bounds on the Error of Query Sets Under the Differentially-Private Matrix Mechanism

A common goal of privacy research is to release synthetic data that satisfies a formal privacy guarantee and can be used by an analyst in place of the original data. To achieve reasonable accuracy, a synthetic data set must be tuned to support a specified set of queries accurately, sacrificing fidelity for other queries. This work considers methods for producing synthetic data under differential privacy and investigates what makes a set of queries "easy" or "hard" to answer. We consider this issue in the particular case of answering sets of linear counting queries using the matrix mechanism (Li et al. 2010), a recent differentially-private mechanism that can reduce error by adding complex correlated noise adapted to a specified workload. Our main result is a novel lower bound on the minimum total error required to simultaneously release answers to a set of workload queries when using the matrix mechanism. The bound reveals that the hardness of a query workload is related to the spectral properties of the workload when it is represented in matrix form. Under (??, ?)-differential privacy, we prove that this bound is tight for many common workloads such as the set of all predicate queries and the set of all k-way marginals. Our empirical study also indicates this bound is close-to-tight on workloads consisting of random interval queries or random marginals.

Online Network Traffic Classification Algorithm Based on RVM

Since compared with the Support Vector Machine (SVM), the Relevance Vector Machine (RVM) not only has the advantage of avoiding the over- learn which is the characteristic of the SVM, but also greatly reduces the amount of computation of the kernel function and avoids the defects of the SVM that the scarcity is not strong, the large amount of calculation as well as the kernel function must satisfy the Mercer's condition and that human empirically determined parameters, so we proposed a new online traffic classification algorithm base on the RVM for this purpose. Through the analysis of the basic principles of RVM and the steps of the modeling, we made use of the training traffic classification model of the RVM to identify the network traffic in the real time through this model and the “port number+ DPI”. When the RVM predicts that the probability is in the query interval, we jointly used the port number and DPI. Finally, we made a detailed experimental validation which shows that: compared with the Support Vector Machine (SVM) network traffic classification algorithm, this algorithm can achieve the online network traffic classification, and the classification predication probability is greatly improved.

A learning theory approach to noninteractive database privacy

In this article, we demonstrate that, ignoring computational constraints, it is possible to release synthetic databases that are useful for accurately answering large classes of queries while preserving differential privacy. Specifically, we give a mechanism that privately releases synthetic data useful for answering a class of queries over a discrete domain with error that grows as a function of the size of the smallest net approximately representing the answers to that class of queries. We show that this in particular implies a mechanism for counting queries that gives error guarantees that grow only with the VC-dimension of the class of queries, which itself grows at most logarithmically with the size of the query class. We also show that it is not possible to release even simple classes of queries (such as intervals and their generalizations) over continuous domains with worst-case utility guarantees while preserving differential privacy. In response to this, we consider a relaxation of the utility guarantee and give a privacy preserving polynomial time algorithm that for any halfspace query will provide an answer that is accurate for some small perturbation of the query. This algorithm does not release synthetic data, but instead another data structure capable of representing an answer for each query. We also give an efficient algorithm for releasing synthetic data for the class of interval queries and axis-aligned rectangles of constant dimension over discrete domains.

Ranking large temporal data

Ranking temporal data has not been studied until recently, even though ranking is an important operator (being promoted as a first-class citizen) in database systems. However, only the instant top- k queries on temporal data were studied in, where objects with the k highest scores at a query time instance t are to be retrieved. The instant top- k definition clearly comes with limitations (sensitive to outliers, difficult to choose a meaningful query time t ). A more flexible and general ranking operation is to rank objects based on the aggregation of their scores in a query interval, which we dub the aggregate top- k query on temporal data. For example, return the top-10 weather stations having the highest average temperature from 10/01/2010 to 10/07/2010; find the top-20 stocks having the largest total transaction volumes from 02/05/2011 to 02/07/2011. This work presents a comprehensive study to this problem by designing both exact and approximate methods (with approximation quality guarantees). We also provide theoretical analysis on the construction cost, the index size, the update and the query costs of each approach. Extensive experiments on large real datasets clearly demonstrate the efficiency, the effectiveness, and the scalability of our methods compared to the baseline methods.

Cache invalidation strategies for Internet-based vehicular ad hoc networks

Internet-based vehicular ad hoc network ( Ivanet) is an emerging technique that combines a wired Internet and a vehicular ad hoc network ( Vanet) for developing an ubiquitous communication infrastructure and improving universal information and service accessibility. A key design optimization technique in Ivanets is to cache the frequently accessed data items in a local storage of vehicles. Since vehicles are not critically limited by the storage/memory space and power consumption, selecting proper data items for caching is not very critical. Rather, an important design issue is how to keep the cached copies valid when the original data items are updated. This is essential to provide fast access to valid data for fast moving vehicles. In this paper, we propose a cooperative cache invalidation (CCI) scheme and its enhancement (ECCI) that take advantage of the underlying location management scheme to reduce the number of broadcast operations and the corresponding query delay. We develop an analytical model for CCI and ECCI techniques for fasthand estimate of performance trends and critical design parameters. Then, we modify two prior cache invalidation techniques to work in Ivanets: a poll-each-read (PER) scheme, and an extended asynchronous (EAS) scheme. We compare the performance of four cache invalidation schemes as a function of query interval, cache update interval, and data size through extensive simulation. Our simulation results indicate that the proposed schemes can reduce the query delay up to 69% and increase the cache hit rate up to 57%, and have the lowest communication overhead compared to the prior PER and EAS schemes.

다양한 응용의 스트림 데이터 모니터링을 위한 연속질의 모델링

다양한 센서들로부터 실시간으로 수집되는 데이터를 분석, 처리해야하는 유비쿼터스 환경의 다양한 응용서비스(모니터링 서비스)를 개발하기 위해서는 반드시 연속질의 처리 방법이 요구된다. 기존 연구에서 연속질의는 대부분 간격질의와 영역질의만으로 표현되고, 이러한 질의에 대한 처리 기법들이 제시되었다. 그러나 연속질의의 종류는 매우 다양하며, 속성 조건, 공간 조건, 시간 조건의 결합을 통해 표현될 수 있다. 이 논문에서는 연속질의의 종류를 분류하고, 여러 조건들의 결합을 통해 표현 가능한 연속질의 모델을 제안하고자 한다. 이 논문은 연속질의를 표현하는 질의 모델을 제안함으로써, 연속질의 처리를 위한 다양한 후속 연구에 대한 방향을 제시하는데 기여하였다. Techniques for processing continuous queries are required to developing the various types of application services (monitoring services) in ubiquitous environment where the real-time data acquisition from a lot of sensors, analysis, and processing are required. In the previous works of the continuous queries, they have represented all of the continuous queries as the interval queries or region queries, and proposed some methods for processing theses queries. The types of continuous queries, however, are very various, and could be presented by combining the attribute conditions, spatial conditions, and temporal conditions. In this paper, I have classify the types of continuous queries, and have proposed the continuous query model which could be presented by combining those conditions. The contributions of this paper include that it proposes the query model representing the continuous queries and suggests future research directions.

Large-scale similarity data management with distributed Metric Index

Metric space is a universal and versatile model of similarity that can be applied in various areas of non-text information retrieval. However, a general, efficient and scalable solution for metric data management is still a resisting research challenge. In this work, we try to make an important step towards such management system that would be able to scale to data collections of billions of objects. We propose a distributed index structure for similarity data management called the Metric Index (M-Index) which can answer queries in precise and approximate manner. This technique can take advantage of any distributed hash table that supports interval queries and utilize it as an underlying index. We have performed numerous experiments to test various settings of the M-Index structure and we have proved its usability by developing a full-featured publicly-available Web application.

Maximizing area throughput in clustered wireless sensor networks

In this paper we present a mathematical model to study a multi-sink Wireless Sensor Network (WSN). Both sensors and sinks are assumed to be Poisson distributed in a given finite domain. Sinks send periodic queries, and each sensor transmits its sample to a sink, selected among those that are audible, thus creating a clustered network. Our aim is to describe how the Area Throughput, defined as the amount of samples per unit of time successfully transmitted to the sinks from the given area, depends on the density of sensors and the query interval. We jointly account for radio channel, Physical (PHY), Medium Access Control (MAC) and Network (NET) aspects (i.e., different network topologies, packet collisions, power losses and radio channel behaviour), and we compare the performance of two different simple data aggregation strategies. Performance is evaluated by varying the traffic offered to the network (i.e., the density of sensors deployed), the packet size, and, by considering IEEE 802.15.4 as a reference case, the number of Guaranteed Time Slots allocated, and the Superframe Order. The mathematical model shows how the Area Throughput can be optimized.

Determining relevance of imprecise temporal intervals for cultural heritage information retrieval

Time is an essential concept in cultural heritage applications. Instances of temporal concepts such as time intervals are used for the annotation of cultural objects and also for querying datasets containing information about these objects. Hence it is important to match query and annotation intervals by examining their similarity or closeness. One of the problems is that in many cases time intervals are imprecise. For example, the boundaries of the “Pre-Roman age” and the “Roman age” are inherently imprecise and it may be difficult to distinguish them with clear-cut intervals. In this paper we apply the fuzzy set theory to model imprecise time intervals in order to determine relevance of the relationship between two time intervals. We present a method for matching query and annotation intervals based on their weighted mutual overlapping and closeness. We present (1) methods for calculating these weights to produce a combined measure and (2) results of comparing the combined measure with human evaluators as a case study. The case study takes into consideration archaeological temporal information, which is in most cases inherently fuzzy, and therefore offers a particularly complex and challenging scenario. The results show that our new combined measure that utilizes different weighted measures together in rankings, performs the best in terms of precision and recall. It should be used when ranking annotation intervals according to a given query interval in cultural heritage information retrieval. Our approach intends to be generalizable: overlapping and closeness may be calculated between any two fuzzy temporal intervals. The presented procedure of using user evaluation results as a basis for assigning weights for overlapping and closeness could potentially be used to reveal weights in other domains and purposes as well.

Performance analysis of opportunistic broadcast for delay-tolerant wireless sensor networks

This paper investigates a class of mobile wireless sensor networks that are unconnected most of the times; we refer to them as delay–tolerant wireless sensor networks (DTWSN). These networks inherit their characteristics from both delay tolerant networks (DTN) and traditional wireless sensor networks. After introducing DTWSNs, three main problems in the design space of these networks are discussed: routing, data gathering, and neighbor discovery. A general protocol is proposed for DTWSNs based on opportunistic broadcasting in delay-tolerant networks with radio device on–off periods. Three performance measures are defined in the study: the energy for sending queries to ask for data from possible neighbors (querying energy), data transfer energy, and absorption time (delay). A simple yet accurate approximation for the data-transfer energy is proposed. An analytic model is provided to evaluate the querying energy per contact ( epc). Simulation results for the data propagation delay show that the querying energy per contact measure obtained from the analytic model is proportional to the product of the querying energy and the delay. A practical rule of thumb for an optimal query interval in terms of delay and energy is derived from different parts of the study.

Finding Range Minima in the Middle: Approximations and Applications

A Range Minimum Query asks for the position of a minimal element between two specified array-indices. We consider a natural extension of this, where our further constraint is that if the minimum in a query interval is not unique, then the query should return an approximation of the median position among all positions that attain this minimum. We present a succinct preprocessing scheme using Dn + o(n) bits in addition to the static input array (small constant D), such that subsequent “range median of minima queries” can be answered in constant time. This data structure can be built in linear time, with little extra space needed at construction time. We introduce several new combinatorial concepts such as Super-Cartesian Trees and Super-Ballot Numbers. We give applications of our preprocessing scheme in text indexes such as (compressed) suffix arrays and trees.